State of health estimation and remaining useful lifetime prediction of battery based on the real dynamic forklift profile

It is challenging and valuable to estimate the state of health (SOH) and predict the remaining useful lifetime (RUL) of lithium-ion batteries under random, dynamic, and real operation conditions. This work aims to estimate the SOH of a battery aged using a realistic forklift load profile. Furthermore, the influence on the SOH estimation of the feature extraction and selection based on different positions of one recharging curve was investigated and the RUL of the battery was predicted based on the limited capacities. Filtering methods and support vector regression (SVR)-based subset selection methods were used in sequence to select the final features for battery SOH estimation. The impact of features on the output of SVR-based SOH estimation was analyzed through Partial dependence plots (PDP). The results showed that the proposed feature selection can reduce SOH estimation error from values larger than 5.43% to less than 3.65%. Estimation accuracy is below 1%RMSE based on one sub-curve even the data in the later aging stage not involved in training. Gaussian process regression (GPR) combined with random sampling was used to predict the RUL and uncertainty. The minimum absolute error of RUL prediction was 0.379 FECs.